This document goes into detail about how multiclass machine learning algorithms can be used to determine if a suit should be bid on (or not) in a Euchre round, and to determine what factors contribute to a good hand.

The project, Diabetes Prediction Analysis Using Machine Learning in R, utilized the Pima Indians Diabetes dataset from Kaggle. This dataset consists of 765 rows and 9 features: pregnancies, glucose levels, blood pressure, skin thickness, insulin levels, BMI, diabetes pedigree function, age, and the outcome variable indicating diabetes presence. The goal was to evaluate and compare the performance of various machine learning models for diabetes prediction and identify key predictive features.

Recreating and improving a FiveThirtyEight Graph using techniques from Intro to Data Science

Process for splitting single column into multiple using a delimiter and parsing out non-numeric character values from resulting columns

This interactive Shiny application enables an in-depth exploration of drug effectiveness and side effects using the UCI Drug Review dataset. It provides a range of analytical features, such as sentiment analysis of user reviews, visualization of drug ratings distribution, and a comparison of side effects via word clouds. Users can filter data by conditions and drugs, offering insights into the drug’s effectiveness, common side effects, and statistical trends across various conditions. The application aims to support healthcare professionals and researchers in evaluating drug performance based on real-world user feedback.

Histogram of ptid